Expert algorithm based on adaptive particle swarm optimization for power flow analysis

An Expert algorithm based on particle swarm optimization (PSO) technique with adaptive PSO parameters has been developed for power flow analysis under critical conditions and multiple power flow solutions. Depending on the objective functions of the current and best solutions in the present generation, unique and innovative formulas are designed for two sets of PSO parameters, inertia weight and learning factors. For faster, sure convergence and overcome the limitations of conventional methods, PSO parameters are so designed that they are highly adaptive. To the best of our knowledge, it is the first report of applying Adaptive PSO (APSO) to solve power flow problems. Multiple power flow solutions which are used for voltage stability analysis can be obtained if proposed method is used with local search as accelerating technique. The proposed algorithm proves its robustness providing reliable and better convergence under high R/X ratios and maximum loadability limits. The effectiveness and efficiency has been established showing results for standard and ill-conditioned test systems.

[1]  Y. Tamura,et al.  A Load Flow Calculation Method for Ill-Conditioned Power Systems , 1981, IEEE Transactions on Power Apparatus and Systems.

[2]  Swapan Kumar Goswami,et al.  Simple but Reliable Two-Stage GA Based Load Flow , 2007 .

[3]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[4]  O. Alsac,et al.  Fast Decoupled Load Flow , 1974 .

[5]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[6]  Venkataramana Ajjarapu,et al.  The continuation power flow: a tool for steady state voltage stability analysis , 1991 .

[7]  Parimal Acharjee,et al.  Robust load flow based on local search , 2008, Expert Syst. Appl..

[8]  William F. Tinney,et al.  Power Flow Solution by Newton's Method , 1967 .

[9]  A. Li,et al.  Development of constrained-genetic-algorithm load-flow method , 1997 .